{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d407dc7",
   "metadata": {},
   "source": [
    "# Compute P-/S-wave travel Times\n",
    "\n",
    "In this notebook, we compute the table of P-/S-wave travel times from a 3D grid of potential source locations to every seismic station. This travel time table is necessary for backprojection and for earthquake location with `NLLoc`.\n",
    "\n",
    "This tutorial uses the `pykonal` package to compute travel times in a given velocity model (see `pykonal` documentation at [https://github.com/malcolmw/pykonal](https://github.com/malcolmw/pykonal)). Please acknowledge [White et al. (2020)](https://pubs.geoscienceworld.org/ssa/srl/article-abstract/91/4/2378/586804/PyKonal-A-Python-Package-for-Solving-the-Eikonal?redirectedFrom=fulltext) if using `pykonal`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "053d6969",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# choose the number of threads you want to limit the computation to\n",
    "n_CPUs = 24\n",
    "os.environ[\"OMP_NUM_THREADS\"] = str(n_CPUs)\n",
    "\n",
    "import BPMF\n",
    "import h5py as h5\n",
    "import numpy as np\n",
    "import pykonal\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "\n",
    "from BPMF import NLLoc_utils\n",
    "from scipy.interpolate import interp1d, LinearNDInterpolator\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0888463d",
   "metadata": {},
   "source": [
    "## Load station metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9b071c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "NETWORK_FILENAME = \"network.csv\"\n",
    "TT_FILENAME = \"tts.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c426fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read network metadata\n",
    "net = BPMF.dataset.Network(NETWORK_FILENAME)\n",
    "net.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8be78738",
   "metadata": {},
   "source": [
    "## Read the velocity model from Karabulut et al. 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cbfa111",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we assume that you are running this notebook from Seismic_BPMF/notebooks/ and the velocity model\n",
    "# is stored at Seismic_BPMF/data/\n",
    "FILEPATH_VEL_MODEL = os.path.join(os.pardir, \"data\", \"velocity_model_Karabulut2011.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac6b2d7",
   "metadata": {},
   "source": [
    "We read the velocity model with `pandas`. Not all columns of the csv files are necessary for our purpose. The velocity model is a 1D layered model and the `depth` column gives the top of each layer in meters (positive downward). The `P` and `S` columns are the wave velocities in m/s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e87583a2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>depth</th>\n",
       "      <td>-2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
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       "      <td>22000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>77000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>2900.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>5950.0</td>\n",
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       "      <td>6300.0</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>8045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>1670.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>3150.0</td>\n",
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       "      <td>3660.0</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>4650.0</td>\n",
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      "text/plain": [
       "           0       1       2       3       4       5       6       7       8   \\\n",
       "depth -2000.0     0.0  1000.0  2000.0  3000.0  4000.0  5000.0  6000.0  8000.0   \n",
       "P      2900.0  3000.0  5600.0  5700.0  5800.0  5900.0  5950.0  6050.0  6100.0   \n",
       "S      1670.0  1900.0  3150.0  3210.0  3260.0  3410.0  3420.0  3440.0  3480.0   \n",
       "\n",
       "            9        10       11       12       13       14       15       16  \\\n",
       "depth  10000.0  12000.0  14000.0  15000.0  20000.0  22000.0  25000.0  32000.0   \n",
       "P       6150.0   6200.0   6250.0   6300.0   6400.0   6500.0   6700.0   8000.0   \n",
       "S       3560.0   3590.0   3610.0   3630.0   3660.0   3780.0   3850.0   4650.0   \n",
       "\n",
       "            17  \n",
       "depth  77000.0  \n",
       "P       8045.0  \n",
       "S       4650.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the velocity model with pandas\n",
    "velocity_model = pd.read_csv(\n",
    "    FILEPATH_VEL_MODEL, \n",
    "    usecols=[1, 2, 4],\n",
    "    names=[\"depth\", \"P\", \"S\"],\n",
    "    skiprows=1,\n",
    "    )\n",
    "velocity_model.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71fa08d5",
   "metadata": {},
   "source": [
    "## Build a 3D Grid of P-/S-wave Velocities\n",
    "\n",
    "Even though the model used here is a 1D model, `pykonal` works with a 3D model. Therefore, we first need to define the velocity model on a 3D spatial grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fc86f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define constant to go from degrees to radians\n",
    "DEG_TO_RAD = np.pi / 180.\n",
    "# define the geographical extent of the 3D grid\n",
    "LON_MIN_DEG = 30.20\n",
    "LON_MAX_DEG = 30.45\n",
    "LAT_MIN_DEG = 40.60\n",
    "LAT_MAX_DEG = 40.80\n",
    "DEP_MIN_KM = -2.0\n",
    "DEP_MAX_KM = 30.\n",
    "# define the grid spacing\n",
    "D_LON_DEG = 0.01 \n",
    "D_LAT_DEG = 0.01\n",
    "D_LON_RAD = D_LON_DEG * DEG_TO_RAD\n",
    "D_LAT_RAD = D_LAT_DEG * DEG_TO_RAD\n",
    "D_DEP_KM = 0.5"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8d4db055",
   "metadata": {},
   "source": [
    "All stations must be located within the travel-time grid, otherwise PyKonal returns `inf` travel times for those stations that are outside the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d1d05c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if nothing happens here, it means that everything is alright\n",
    "for s in range(net.n_stations):\n",
    "    if (net.longitude[s] < LON_MIN_DEG) or (net.longitude[s] > LON_MAX_DEG):\n",
    "        print(f\"The longitude of station {net.stations[s]} is outside the grid! Make your grid bigger.\")\n",
    "    if (net.latitude[s] < LAT_MIN_DEG) or (net.latitude[s] > LAT_MAX_DEG):\n",
    "        print(f\"The latitude of station {net.stations[s]} is outside the grid! Make your grid bigger.\")\n",
    "    if (net.depth[s] < DEP_MIN_KM) or (net.depth[s] > DEP_MAX_KM):\n",
    "        print(f\"The depth of station {net.stations[s]} is outside the grid! Make your grid bigger.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2d14b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will be using pykonal in spherical coordinates with radial coordinates (depth) in km\n",
    "# therefore, we first convert all meters in kilometers\n",
    "velocity_model[\"depth\"] /= 1000.0\n",
    "velocity_model[\"P\"] /= 1000.0\n",
    "velocity_model[\"S\"] /= 1000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e9f75bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to expand the 1D layered velocity model onto a 3D grid,\n",
    "# we build an interpolator that gives the velocity at any point in depth\n",
    "# to preserve the layered structure in the interpolated model, we need to\n",
    "# add new rows to the 1D model to keep the velocity discontinuities\n",
    "\n",
    "# new depths just below the existing depths\n",
    "new_depths = velocity_model[\"depth\"][1:] - 0.00001\n",
    "# P-wave velocity interpolator\n",
    "new_vP = velocity_model[\"P\"][:-1]\n",
    "interpolator_P = interp1d(\n",
    "    np.hstack((velocity_model[\"depth\"], new_depths)),\n",
    "    np.hstack((velocity_model[\"P\"], new_vP)),\n",
    ")\n",
    "# S-wave velocity interpolator\n",
    "new_vS = velocity_model[\"S\"][:-1]\n",
    "interpolator_S = interp1d(\n",
    "    np.hstack((velocity_model[\"depth\"], new_depths)),\n",
    "    np.hstack((velocity_model[\"S\"], new_vS)),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc79f87",
   "metadata": {},
   "source": [
    "The spherical coordinate system used by `pykonal` specifies a point position with $(r, \\theta, \\varphi)$:\n",
    "- $r$: Distance from center or Earth in km (= decreasing depth).\n",
    "- $\\theta$: Polar angle in radians (= co-latitude or, equivalently, decreasing latitude).\n",
    "- $\\varphi$: Azimuthal angle in radians (= longitude).\n",
    "\n",
    "Thus, the 3D grid must be built such that $r$, $\\theta$ and $\\varphi$ increase with increasing index. Axis 0 is decreasing depth, axis 1 is decreasing latitude and axis 2 is increasing longitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "002daac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Increasing longitudes:  [30.2  30.21 30.22 30.23 30.24 30.25 30.26 30.27 30.28 30.29 30.3  30.31\n",
      " 30.32 30.33 30.34 30.35 30.36 30.37 30.38 30.39 30.4  30.41 30.42 30.43\n",
      " 30.44 30.45]\n",
      "Decreasing latitudes:  [40.8  40.79 40.78 40.77 40.76 40.75 40.74 40.73 40.72 40.71 40.7  40.69\n",
      " 40.68 40.67 40.66 40.65 40.64 40.63 40.62 40.61 40.6 ]\n",
      "Decreasing depths:  [30.  29.5 29.  28.5 28.  27.5 27.  26.5 26.  25.5 25.  24.5 24.  23.5\n",
      " 23.  22.5 22.  21.5 21.  20.5 20.  19.5 19.  18.5 18.  17.5 17.  16.5\n",
      " 16.  15.5 15.  14.5 14.  13.5 13.  12.5 12.  11.5 11.  10.5 10.   9.5\n",
      "  9.   8.5  8.   7.5  7.   6.5  6.   5.5  5.   4.5  4.   3.5  3.   2.5\n",
      "  2.   1.5  1.   0.5  0.  -0.5 -1.  -1.5 -2. ]\n"
     ]
    }
   ],
   "source": [
    "# make sure the user-specified ends of the grid are included in the arange vectors\n",
    "longitudes = np.arange(LON_MIN_DEG, LON_MAX_DEG+D_LON_DEG/2., D_LON_DEG)\n",
    "latitudes = np.arange(LAT_MAX_DEG, LAT_MIN_DEG-D_LAT_DEG/2., -D_LAT_DEG)\n",
    "depths = np.arange(DEP_MAX_KM, DEP_MIN_KM-D_DEP_KM/2., -D_DEP_KM)\n",
    "\n",
    "print(\"Increasing longitudes: \", longitudes)\n",
    "print(\"Decreasing latitudes: \", latitudes)\n",
    "print(\"Decreasing depths: \", depths)\n",
    "\n",
    "# get the number of points in each direction\n",
    "n_longitudes = len(longitudes)\n",
    "n_latitudes = len(latitudes)\n",
    "n_depths = len(depths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "97a831e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the previously defined interpolators to compute the P-/S-wave velocity\n",
    "# at each point of the 3D spherical coordinate grid defined by depths, latitudes, longitudes (see above cell)\n",
    "vP_grid = np.zeros((n_depths, n_latitudes, n_longitudes), dtype=np.float32)\n",
    "vS_grid = np.zeros((n_depths, n_latitudes, n_longitudes), dtype=np.float32)\n",
    "for i in range(n_depths):\n",
    "    vP_grid[i, :, :] = interpolator_P(depths[i])\n",
    "    vS_grid[i, :, :] = interpolator_S(depths[i])\n",
    "\n",
    "depths_g, latitudes_g, longitudes_g = np.meshgrid(\n",
    "    depths, latitudes, longitudes, indexing=\"ij\"\n",
    ")\n",
    "\n",
    "# keep velocities and grid point coordinates in a dictionary for later use\n",
    "grid = {}\n",
    "grid[\"vP\"] = vP_grid\n",
    "grid[\"vS\"] = vS_grid\n",
    "grid[\"longitude\"] = longitudes_g\n",
    "grid[\"latitude\"] = latitudes_g\n",
    "grid[\"depth\"] = depths_g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c607dc",
   "metadata": {},
   "source": [
    "The 3D grid is ready! This piece of code can easily be adapted for using a 2D or 3D input velocity model. Keep in mind that, in most cases, even a 3D velocity model will have to be re-ordered to satisfy `pykonal` conventions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62302530",
   "metadata": {},
   "source": [
    "## Compute the P-/S-wave travel times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38170696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min radius: 6341 km, min co-latitude: 0.86 rad, min azimuthal angle: 0.53 rad\n"
     ]
    }
   ],
   "source": [
    "# define the origin of our grid for pykonal\n",
    "# the origin of the grid is the point with lowest (r, theta, phi)\n",
    "r_min, theta_min, phi_min = pykonal.transformations.geo2sph(\n",
    "    [grid[\"latitude\"].max(), grid[\"longitude\"].min(), grid[\"depth\"].max()]\n",
    ")\n",
    "print(\n",
    "    \"Min radius: {:.0f} km, min co-latitude: {:.2f} rad, min azimuthal angle: {:.2f} rad\".format(\n",
    "        r_min, theta_min, phi_min\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "55121714",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing travel times: 0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing travel times: 8it [00:06,  1.23it/s]\n"
     ]
    }
   ],
   "source": [
    "# store travel times in a dictionary\n",
    "tts = {\"tt_P\": {}, \"tt_S\": {}}\n",
    "for s, network_sta in tqdm(enumerate(net.stations), desc=\"Computing travel times\"):\n",
    "    for phase in [\"P\", \"S\"]:\n",
    "        # initialize travel time arrays\n",
    "        tts[f\"tt_{phase}\"][network_sta] = np.zeros(\n",
    "            grid[f\"v{phase}\"].shape, dtype=np.float32\n",
    "        )\n",
    "\n",
    "        # initialize the Eikonal equation solver\n",
    "        solver = pykonal.solver.PointSourceSolver(coord_sys=\"spherical\")\n",
    "        # define the computational grid so that it matches exactly\n",
    "        # the grid on which we have expanded the velocity model\n",
    "        solver.velocity.min_coords = r_min, theta_min, phi_min\n",
    "        solver.velocity.node_intervals = D_DEP_KM, D_LAT_RAD, D_LON_RAD\n",
    "        solver.velocity.npts = n_depths, n_latitudes, n_longitudes\n",
    "        solver.velocity.values = grid[f\"v{phase}\"]\n",
    "\n",
    "        # we take the station as the seismic source to compute the\n",
    "        # travel times to all points of the grid\n",
    "        source_latitude = net.latitude[s]\n",
    "        source_longitude = net.longitude[s]\n",
    "        source_depth = net.depth[s]\n",
    "        # convert the geographical coordinates to spherical coordinates\n",
    "        r_source, theta_source, lbd_source = pykonal.transformations.geo2sph(\n",
    "            [source_latitude, source_longitude, source_depth]\n",
    "        )\n",
    "        solver.src_loc = np.array([r_source, theta_source, lbd_source])\n",
    "\n",
    "        # solve the Eikonal equation\n",
    "        solver.solve()\n",
    "\n",
    "        tts[f\"tt_{phase}\"][network_sta] = solver.tt.values[...]\n",
    "\n",
    "# store source coordinates in tts:\n",
    "tts[\"source_coordinates\"] = {}\n",
    "src_coords = pykonal.transformations.sph2geo(solver.velocity.nodes)\n",
    "tts[\"source_coordinates\"][\"latitude\"] = src_coords[..., 0]\n",
    "tts[\"source_coordinates\"][\"longitude\"] = src_coords[..., 1]\n",
    "tts[\"source_coordinates\"][\"depth\"] = src_coords[..., 2]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb9a0db6",
   "metadata": {},
   "source": [
    "## Store travel time table for backprojection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee151b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write travel-times in hdf5 file\n",
    "with h5.File(os.path.join(BPMF.cfg.MOVEOUTS_PATH, TT_FILENAME), mode=\"w\") as f:\n",
    "    for key1 in tts.keys():\n",
    "        f.create_group(key1)\n",
    "        for key2 in tts[key1].keys():\n",
    "            f[key1].create_dataset(key2, data=tts[key1][key2], compression=\"gzip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc9aeb5",
   "metadata": {},
   "source": [
    "## Store travel time tables for `NLLoc`\n",
    "\n",
    "We write the travel time tables in an adequate format for `NLLoc` using the utility functions from `BPMF.NLLoc_utils`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0816981c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The origin of the grid is: 30.2000, 40.6000, -2.000km\n",
      "Longitude spacing: 0.010deg\n",
      "Latitude spacing: 0.010deg\n",
      "Depth spacing: 0.500km\n",
      "Station DE07\n",
      "--- Phase P\n",
      "--- Phase S\n",
      "Station DC07\n",
      "--- Phase P\n",
      "--- Phase S\n",
      "Station DC08\n",
      "--- Phase P\n",
      "--- Phase S\n",
      "Station DE08\n",
      "--- Phase P\n",
      "--- Phase S\n",
      "Station SPNC\n",
      "--- Phase P\n",
      "--- Phase S\n",
      "Station DC06\n",
      "--- Phase P\n",
      "--- Phase S\n",
      "Station SAUV\n",
      "--- Phase P\n",
      "--- Phase S\n",
      "Station DD06\n",
      "--- Phase P\n",
      "--- Phase S\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# use the functions from ``\n",
    "# save travel time tables for NLLoc\n",
    "longitude, latitude, depth, tts_nlloc = BPMF.NLLoc_utils.load_pykonal_tts(\n",
    "    TT_FILENAME, path=BPMF.cfg.MOVEOUTS_PATH\n",
    ")\n",
    "\n",
    "BPMF.NLLoc_utils.write_NLLoc_inputs(\n",
    "    longitude,\n",
    "    latitude,\n",
    "    depth,\n",
    "    tts_nlloc,\n",
    "    net,\n",
    "    output_path=BPMF.cfg.NLLOC_INPUT_PATH,\n",
    "    basename=BPMF.cfg.NLLOC_BASENAME,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de55fee8",
   "metadata": {},
   "source": [
    "## Plot the travel time field for a given station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6b3bddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy as ctp\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.cm import ScalarMappable\n",
    "from scipy.interpolate import interpn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea801e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define map parameters\n",
    "\n",
    "# define inset extent\n",
    "LAT_MIN_INSET, LAT_MAX_INSET = 36.00, 42.00\n",
    "LON_MIN_INSET, LON_MAX_INSET = 22.00, 36.00\n",
    "# define projections\n",
    "data_coords = ctp.crs.PlateCarree()\n",
    "projection = ctp.crs.Mercator(\n",
    "    central_longitude=(LON_MIN_DEG + LON_MAX_DEG) / 2.0,\n",
    "    min_latitude=LAT_MIN_DEG,\n",
    "    max_latitude=LAT_MAX_DEG,\n",
    ")\n",
    "projection_inset = ctp.crs.Mercator(\n",
    "    central_longitude=(LON_MIN_INSET + LON_MAX_INSET) / 2.0,\n",
    "    min_latitude=LAT_MIN_INSET,\n",
    "    max_latitude=LAT_MAX_INSET,\n",
    ")\n",
    "\n",
    "source_coords = {}\n",
    "source_coords[\"longitude\"] = longitudes_g\n",
    "source_coords[\"latitude\"] = latitudes_g\n",
    "source_coords[\"depth\"] = depths_g\n",
    "\n",
    "shape_3D = (n_depths, n_latitudes, n_longitudes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e70454d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %config InlineBackend.figure_formats = ['svg']\n",
    "\n",
    "# parameters for ray plotting\n",
    "RAY_LON_SPACING_DEG = 0.1\n",
    "RAY_LAT_SPACING_DEG = 0.1\n",
    "\n",
    "# plot map\n",
    "fig = plt.figure(\"map_stations_grid\", figsize=(15, 15))\n",
    "ax = fig.add_subplot(111, projection=projection)\n",
    "ax.set_rasterization_zorder(1.0)\n",
    "ax.set_extent([LON_MIN_DEG, LON_MAX_DEG, LAT_MIN_DEG, LAT_MAX_DEG])\n",
    "\n",
    "ax = BPMF.plotting_utils.initialize_map(\n",
    "    [LON_MIN_DEG, LON_MAX_DEG],\n",
    "    [LAT_MIN_DEG, LAT_MAX_DEG],\n",
    "    map_axis=ax,\n",
    "    seismic_stations=net.metadata,\n",
    ")\n",
    "\n",
    "BPMF.plotting_utils.add_scale_bar(ax, 0.05, 0.15, 10.0, projection)\n",
    "\n",
    "# plot inset map\n",
    "axins = inset_axes(\n",
    "    ax,\n",
    "    width=\"40%\",\n",
    "    height=\"30%\",\n",
    "    loc=\"lower right\",\n",
    "    axes_class=ctp.mpl.geoaxes.GeoAxes,\n",
    "    axes_kwargs=dict(map_projection=projection_inset),\n",
    ")\n",
    "axins.set_rasterization_zorder(1.5)\n",
    "axins.set_extent(\n",
    "    [LON_MIN_INSET, LON_MAX_INSET, LAT_MIN_INSET, LAT_MAX_INSET], crs=data_coords\n",
    ")\n",
    "# study_region = geometry.box(minx=lon_min, maxx=lon_max, miny=lat_min, maxy=lat_max)\n",
    "# axins.add_geometries([study_region], crs=data_coords, edgecolor=\"k\", facecolor=\"C0\")\n",
    "axins.plot(\n",
    "    (LON_MIN_DEG + LON_MAX_DEG) / 2.0,\n",
    "    (LAT_MIN_DEG + LAT_MAX_DEG) / 2.0,\n",
    "    marker=\"s\",\n",
    "    color=\"C0\",\n",
    "    markersize=10,\n",
    "    markeredgecolor=\"k\",\n",
    "    transform=data_coords,\n",
    ")\n",
    "axins.text(\n",
    "    (LON_MIN_DEG + LON_MAX_DEG) / 2.0 - 0.35,\n",
    "    (LAT_MIN_DEG + LAT_MAX_DEG) / 2.0 - 0.8,\n",
    "    \"Study Region\",\n",
    "    transform=data_coords,\n",
    ")\n",
    "\n",
    "axins.add_feature(ctp.feature.BORDERS)\n",
    "axins.add_feature(ctp.feature.LAND)\n",
    "axins.add_feature(ctp.feature.OCEAN)\n",
    "axins.add_feature(ctp.feature.GSHHSFeature(scale=\"full\", levels=[1], zorder=0.49))\n",
    "BPMF.plotting_utils.add_scale_bar(axins, 0.50, 0.30, 500.0, projection_inset)\n",
    "\n",
    "# -----------------------------------------------\n",
    "#     add side axes for slices of the travel time field\n",
    "divider = make_axes_locatable(ax)\n",
    "\n",
    "cs_lon = divider.append_axes(\"bottom\", size=\"30%\", pad=\"5%\", axes_class=plt.Axes)\n",
    "cs_lat = divider.append_axes(\"right\", size=\"30%\", pad=\"5%\", axes_class=plt.Axes)\n",
    "cax = divider.append_axes(\"top\", size=\"5%\", pad=\"5%\", axes_class=plt.Axes)\n",
    "\n",
    "\n",
    "def cut_through(station_code, lon_spacing=0.1, lat_spacing=0.1, cm=\"inferno\"):\n",
    "    lon_sta = net.metadata.loc[station_code].longitude\n",
    "    lat_sta = net.metadata.loc[station_code].latitude\n",
    "    elv_sta = net.metadata.loc[station_code].elevation_m\n",
    "    station_code = net.metadata.loc[station_code].stations\n",
    "    # depth = np.unique(source_coords[\"depth\"])\n",
    "    # longitude = np.unique(source_coords[\"longitude\"])\n",
    "    # latitude = np.unique(source_coords[\"latitude\"])\n",
    "    # plot station in different color on map\n",
    "    ax.scatter(lon_sta, lat_sta, s=100, marker=\"v\", color=\"C3\", transform=data_coords)\n",
    "    # find closest slice in longitude\n",
    "    idx_lon = np.abs(lon_sta - source_coords[\"longitude\"][0, 0, :]).argmin()\n",
    "    # find closest slice in latitude\n",
    "    idx_lat = np.abs(lat_sta - source_coords[\"latitude\"][0, :, 0]).argmin()\n",
    "    # plot travel-time field\n",
    "    cs_lon.clear()\n",
    "    cs_lat.clear()\n",
    "    # longitudinal slice\n",
    "    slice_lon = np.index_exp[:, idx_lat, :]\n",
    "    new_coords_slice_lon = np.stack(\n",
    "        [\n",
    "            source_coords[\"depth\"][slice_lon].flatten(),\n",
    "            lat_sta\n",
    "            * np.ones(source_coords[\"longitude\"][slice_lon].size, dtype=np.float32),\n",
    "            source_coords[\"longitude\"][slice_lon].flatten(),\n",
    "        ],\n",
    "        axis=1,\n",
    "    )\n",
    "    # interpn is fast but not very flexible: it builds a grid with strictly increasing coordinates\n",
    "    # whereas the travel-times are given with decreasing depth and latitude because these\n",
    "    # correspond to increasing radius and colatitude in spherical coordinates\n",
    "    tt_P_slice_lon = interpn(\n",
    "        (\n",
    "            np.unique(source_coords[\"depth\"]),\n",
    "            np.unique(source_coords[\"latitude\"]),\n",
    "            np.unique(source_coords[\"longitude\"]),\n",
    "        ),\n",
    "        tts[\"tt_P\"][station_code].reshape(shape_3D)[::-1, ::-1, :],\n",
    "        new_coords_slice_lon,\n",
    "    ).reshape(source_coords[\"longitude\"][slice_lon].shape)\n",
    "    # latitudinal slice\n",
    "    slice_lat = np.index_exp[:, :, idx_lon]\n",
    "    new_coords_slice_lat = np.stack(\n",
    "        [\n",
    "            source_coords[\"depth\"][slice_lat].flatten(),\n",
    "            source_coords[\"latitude\"][slice_lat].flatten(),\n",
    "            lon_sta\n",
    "            * np.ones(source_coords[\"longitude\"][slice_lat].size, dtype=np.float32),\n",
    "        ],\n",
    "        axis=1,\n",
    "    )\n",
    "    tt_P_slice_lat = interpn(\n",
    "        (\n",
    "            np.unique(source_coords[\"depth\"]),\n",
    "            np.unique(source_coords[\"latitude\"]),\n",
    "            np.unique(source_coords[\"longitude\"]),\n",
    "        ),\n",
    "        tts[\"tt_P\"][station_code].reshape(shape_3D)[::-1, ::-1, :],\n",
    "        new_coords_slice_lat,\n",
    "    ).reshape(source_coords[\"longitude\"][slice_lat].shape)\n",
    "    # define tt color scale\n",
    "    cnorm = Normalize(vmin=0.0, vmax=max(tt_P_slice_lon.max(), tt_P_slice_lat.max()))\n",
    "    scalar_map = ScalarMappable(norm=cnorm, cmap=cm)\n",
    "    # plot slices\n",
    "    cs_lon.pcolormesh(\n",
    "        source_coords[\"longitude\"][slice_lon],\n",
    "        source_coords[\"depth\"][slice_lon],\n",
    "        tt_P_slice_lon,\n",
    "        cmap=cm,\n",
    "        vmin=cnorm.vmin,\n",
    "        vmax=cnorm.vmax,\n",
    "        rasterized=True,\n",
    "    )\n",
    "    cs_lon.contour(\n",
    "        source_coords[\"longitude\"][slice_lon],\n",
    "        source_coords[\"depth\"][slice_lon],\n",
    "        tt_P_slice_lon,\n",
    "        cmap=\"Greys\",\n",
    "        linestyles=\"--\"\n",
    "    )\n",
    "    cs_lat.pcolormesh(\n",
    "        source_coords[\"depth\"][slice_lat],\n",
    "        source_coords[\"latitude\"][slice_lat],\n",
    "        tt_P_slice_lat,\n",
    "        cmap=cm,\n",
    "        vmin=cnorm.vmin,\n",
    "        vmax=cnorm.vmax,\n",
    "        rasterized=True,\n",
    "    )\n",
    "    cs_lat.contour(\n",
    "        source_coords[\"depth\"][slice_lat],\n",
    "        source_coords[\"latitude\"][slice_lat],\n",
    "        tt_P_slice_lat,\n",
    "        cmap=\"Greys\",\n",
    "        linestyles=\"--\"\n",
    "    )\n",
    "    \n",
    "    # format axis labels\n",
    "    cs_lon.invert_yaxis()\n",
    "    cs_lat.yaxis.set_label_position(\"right\")\n",
    "    cs_lat.yaxis.tick_right()\n",
    "    cs_lon.set_ylabel(\"Depth (km)\")\n",
    "    cs_lon.set_xlabel(\"Longitude (\" \"\\u00b0\" \")\")\n",
    "    cs_lat.set_xlabel(\"Depth (km)\")\n",
    "    cs_lat.set_ylabel(\"Latitude (\" \"\\u00b0\" \")\")\n",
    "    # add station symbols\n",
    "    cs_lon.plot(\n",
    "        lon_sta,\n",
    "        -elv_sta / 1000.0,\n",
    "        marker=\"v\",\n",
    "        color=\"C3\",\n",
    "        markeredgecolor=\"white\",\n",
    "        markersize=20,\n",
    "    )\n",
    "    cs_lat.plot(\n",
    "        -elv_sta / 1000.0,\n",
    "        lat_sta,\n",
    "        marker=\">\",\n",
    "        color=\"C3\",\n",
    "        markeredgecolor=\"white\",\n",
    "        markersize=20,\n",
    "    )\n",
    "\n",
    "    cs_lon.set_xlim(LON_MIN_DEG, LON_MAX_DEG)\n",
    "    cs_lat.set_ylim(LAT_MIN_DEG, LAT_MAX_DEG)\n",
    "\n",
    "    plt.colorbar(\n",
    "        scalar_map, cax=cax, label=\"P-wave travel time (s)\", orientation=\"horizontal\"\n",
    "    )\n",
    "    cax.xaxis.set_label_position(\"top\")\n",
    "    cax.xaxis.tick_top()\n",
    "\n",
    "\n",
    "cut_through(\"SPNC\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a5a04ae5",
   "metadata": {},
   "source": [
    "## Bonus: Do you need a downsampled grid for efficient earthquake detection (backprojection)?\n",
    "\n",
    "If you want to study a large region and computed a large grid (several millions of grid points), earthquake detection with backprojection (see next notebook) will be quite slow. In fact, you might just want to keep those grid points that have significantly different moveouts to avoid losing time in redundant computations. The following cell shows how to use `BPMF.clib.find_similar_sources` to downsample the grid in a \"smart\" way.\n",
    "\n",
    "!! This operation does not overwrite the original grid, which is still available for the accurate earthquake location !!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a85520b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# choose the number of threads you want to limit the computation to\n",
    "n_CPUs = 24\n",
    "os.environ[\"OMP_NUM_THREADS\"] = str(n_CPUs)\n",
    "import BPMF\n",
    "\n",
    "# travel-time phases to read\n",
    "PHASES = [\"P\", \"S\"]\n",
    "# minimum root-mean-square time difference, in samples, below which two grid sources are considered the same\n",
    "MIN_TIME_DIFFERENCE_SAMP = 1 \n",
    "MIN_TIME_DIFFERENCE_SEC = MIN_TIME_DIFFERENCE_SAMP/BPMF.cfg.SAMPLING_RATE_HZ\n",
    "# define the width, in degrees, of the cells used to group proximal grid points\n",
    "# and efficiently search for similar moveouts\n",
    "CELL_WIDTH_DEG = 0.50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "950d5928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start reading travel times...\n",
      "There are 35490 points in the grid.\n"
     ]
    }
   ],
   "source": [
    "# -------------------------------------------\n",
    "#            Load travel-times\n",
    "# -------------------------------------------\n",
    "tts = BPMF.template_search.TravelTimes(\n",
    "    TT_FILENAME,\n",
    "    tt_folder_path=BPMF.cfg.MOVEOUTS_PATH,\n",
    ")\n",
    "print(\"Start reading travel times...\")\n",
    "tts.read(\n",
    "    PHASES,\n",
    "    read_coords=True,\n",
    "    stations=net.stations,\n",
    ")\n",
    "print(f\"There are {tts.num_sources} points in the grid.\")\n",
    "\n",
    "moveouts = tts.get_travel_times_array(units=\"seconds\", relative_to_first=True)\n",
    "moveouts = moveouts.astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c6136c27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 cells in longitude.\n",
      "There are 2 cells in latitude.\n"
     ]
    }
   ],
   "source": [
    "# -------------------------------------------\n",
    "#              Define cells\n",
    "# -------------------------------------------\n",
    "lon_min, lon_max = (\n",
    "    tts.source_coords[\"longitude\"].min(),\n",
    "    tts.source_coords[\"longitude\"].max(),\n",
    ")\n",
    "lat_min, lat_max = (\n",
    "    tts.source_coords[\"latitude\"].min(),\n",
    "    tts.source_coords[\"latitude\"].max(),\n",
    ")\n",
    "cell_longitudes = np.arange(lon_min, lon_max + CELL_WIDTH_DEG, CELL_WIDTH_DEG)\n",
    "cell_latitudes = np.arange(lat_min, lat_max + CELL_WIDTH_DEG, CELL_WIDTH_DEG)\n",
    "\n",
    "print(f\"There are {len(cell_longitudes)} cells in longitude.\")\n",
    "print(f\"There are {len(cell_latitudes)} cells in latitude.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1b51d886",
   "metadata": {},
   "source": [
    "We can now call `BPMF.clib.find_similar_sources` with the given threshold `MIN_TIME_DIFFERENCE_SEC`. This routine returns an array of booleans that has as many elements as there are points in the grid. The sources that are kept in the downsampled grid correspond to the `True` elements of the output array.\n",
    "\n",
    "The following cell should take very little time for the tutorial but it is not always the case for large-scale applications:\n",
    "\n",
    "!! The following cell can take a VERY long time for large grids (~24hours or more). This is a ONE TIME COMPUTATION that you probably want to run outside of this notebook when dealing with a large-scale problem. !!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2584e914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMS threshold: 0.0400, summed squares threshold: 0.0128\n",
      "---------- 1 / 1 ----------\n",
      "Second loop\n",
      "Progress: 0%\n",
      "Progress: 10%\n",
      "Progress: 20%\n",
      "Progress: 30%\n",
      "Progress: 40%\n",
      "Progress: 50%\n",
      "Progress: 60%\n",
      "Progress: 70%\n",
      "Progress: 80%\n",
      "Progress: 90%\n"
     ]
    }
   ],
   "source": [
    "# -------------------------------------------\n",
    "#         Find redundant sources!\n",
    "# -------------------------------------------\n",
    "#sys.exit()\n",
    "redundant_sources = BPMF.clib.find_similar_sources(\n",
    "        moveouts,\n",
    "        tts.source_coords[\"longitude\"].values,\n",
    "        tts.source_coords[\"latitude\"].values,\n",
    "        cell_longitudes,\n",
    "        cell_latitudes,\n",
    "        MIN_TIME_DIFFERENCE_SEC,\n",
    "        # num_stations_for_diff=NUM_STATIONS_FOR_DIFF, # do not use this argument in the tutorial because the network is small\n",
    "        )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9e4064f4",
   "metadata": {},
   "source": [
    "We obtain the indexes of the non-redundant sources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d09ed84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 14124 sources in the downsampled grid vs 35490 in the original grid.\n"
     ]
    }
   ],
   "source": [
    "sparse_grid_indexes = np.arange(len(redundant_sources), dtype=np.int32)[\n",
    "    ~redundant_sources\n",
    "]\n",
    "print(f\"There are {len(sparse_grid_indexes)} sources in the downsampled grid\"\n",
    "      f\" vs {len(redundant_sources)} in the original grid.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1b8d2d21",
   "metadata": {},
   "source": [
    "We need to save the grid source indexes that constitute the downsampled grid to be able to reconstruct this downsampled grid in the future. Here, we save `sparse_grid_indexes` in an `npy` file in the same folder where the travel times are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "67fdc639",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\n",
    "    os.path.join(\n",
    "        BPMF.cfg.MOVEOUTS_PATH, f\"sparse_grid_indexes_{MIN_TIME_DIFFERENCE_SEC:.2f}.npy\"\n",
    "    ),\n",
    "    sparse_grid_indexes,\n",
    ")"
   ]
  }
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